calllens
CallLens enables developers to automate conversation quality assurance using LangGraph. Use list_rubrics to define evaluation criteria, score_dimension for granular analysis, or analyze_transcript to generate comprehensive reports with timestamped evidence. This server provides objective, data-driven insights into behavioral performance directly within your MCP-compatible environment.
Overview
CallLens provides an automated framework for analyzing conversation quality using behavioral rubrics and LangGraph-powered evaluation. By processing transcripts through standardized schemas, it transforms unstructured dialogue into structured performance data with verifiable evidence.
Key Capabilities
- list_rubrics: Retrieves the full library of available behavioral rubrics and their specific evaluation dimensions.
- score_dimension: Evaluates a specific segment of a transcript against a single rubric dimension, returning a formal score and supporting timestamped evidence.
- analyze_transcript: Executes the complete evaluation pipeline to generate a comprehensive performance report for an entire conversation.
Use Cases
- Quality Assurance teams can automate the scoring of support calls against internal communication standards to ensure consistent agent performance.
- Sales coaches can identify specific moments in a transcript where a representative failed to address a customer objection, using the generated evidence for targeted training.
- Researchers can perform longitudinal analysis on conversation datasets to track how specific behavioral markers shift over hundreds of recorded interactions.
Who This Is For
This server is designed for developers, data analysts, and QA engineers who need to programmatically audit human-to-human communication. It is ideal for users with intermediate technical skills who want to integrate objective behavioral scoring into their existing LLM or analytics pipelines.